Building an auxiliary diagnostic and treatment efficacy prediction model for adolescent depression using machine learning based on electroencephalography technology.

Background: Adolescent depression, characterized by high incidence rates and significant recurrence risks, has emerged as a major global public health concern. Current diagnosis relies predominantly on subjective questionnaires, lacking objective biomarkers and presenting challenges for early identification. Although exercise intervention is recommended as a safe and effective first-line non-pharmacological treatment, individual responses vary considerably. Approximately 30-40% of patients experience limited symptom improvement, necessitating reliable neurobiological markers to predict intervention response and enable precision exercise prescription. Resting-state electroencephalography (rs-EEG), with its non-invasive nature, low cost, and high temporal resolution, represents an ideal tool for identifying objective markers of depression. This study aims to construct a high-performance auxiliary diagnostic model for adolescent depression based on static and dynamic functional connectivity features of rs-EEG, combined with machine learning algorithms. It further seeks to identify neurophysiological biomarkers capable of predicting exercise intervention efficacy, thereby providing objective and practical technical support for early identification and individualized non-pharmacological interventions. Methodology: This study employed a machine learning framework combining diagnostic and predictive models. The diagnostic model aimed to identify depressed individuals among healthy adolescents ( Results: Diagnostic model: The XGBoost model demonstrated exceptional performance in distinguishing SCD patients from healthy controls (AUC of 0.994, accuracy of 96.7%). Core biomarkers primarily concentrated on connections within the dorsal attention network (DAN), salience network (SN), and sensorimotor network (SMN) across the Conclusion: This study systematically elucidates for the first time the intricate model-derived neurophysiological patterns of subclinical depression at the multi-frequency brain network level. It confirms the substantial potential of EEG functional connectivity as an objective biomarker while providing an interpretable neurophysiological basis for the precise selection of exercise interventions. These findings deepen our understanding of the early pathophysiology of depression and lay a robust foundation for developing low-cost, non-invasive tools for early screening and personalized treatment decision-making.